The Importance of Data Engineering and Analytics to Sports Teams

Sports in America have always been a popular and integral part of society. With millions of fans following various sports and teams, there is a vast amount of data generated from each game, practice, and player. However, it is not enough to have just raw data; it needs to be collected, analyzed, and turned into insights that can be used to improve teams’ performance and even shape future games.

This is where data engineering comes in, which is the process of collecting, transforming, and storing data in a way that allows for effective analysis and use. By leveraging data engineering techniques, sports teams and organizations can gain a competitive edge and advance American sports.

One way that data engineering can be used in sports is through player performance analysis. By collecting data on player movements, ball trajectories, and other game statistics, teams can identify strengths and weaknesses in individual players’ performances. This data can then be used to develop personalized training plans for each player, leading to improved performance on the field or court.

Another area where data engineering can be applied is in the analysis of game strategy. By analyzing past games and identifying patterns in gameplay, teams can develop effective strategies that take into account their strengths and weaknesses. This can lead to more efficient use of resources and increased success on the field.

Data engineering can also be used to improve fan engagement. By collecting and analyzing data on fan behavior, teams can create personalized experiences for fans and provide them with more relevant and engaging content. This could include personalized merchandise suggestions, targeted marketing campaigns, and even interactive experiences at the stadium.

Overall, the application of data engineering in American sports has the potential to revolutionize the way teams operate and interact with fans. By using data to make informed decisions, teams can optimize player performance, develop more effective game strategies, and provide fans with personalized experiences that keep them engaged and coming back for more.

As the world becomes increasingly data-driven, the role of data engineering in sports will only continue to grow. It will be interesting to see how sports teams and organizations leverage this technology to push the boundaries of what is possible on the field and court.

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Data Revolution: Data Scientist vs. Research Scientist

In the world of data, the roles and responsibilities can be quite ambiguous and complex. With various perspectives to consider, it can be challenging to understand the distinctions between different job titles under the data umbrella. When you are a technical expert, this is much easier to do but when you are from the commercial side this information is not easily accessible or readily on your radar.

This article aims help those of us who are not from a technical background and explain individual nuances of the data revolution. I will discuss the roles, similarities and differences between data scientists and research scientists. By the end of this article, you should have a better grasp of these positions and how they contribute to the larger data ecosystem.

What is a Data Scientist?

A data scientist is an analytics professional who is responsible for collecting, analyzing and interpreting data to help drive decision-making in an organization. The data scientist role combines elements of several traditional and technical jobs, including mathematician, scientist, statistician and computer programmer. It involves the use of advanced analytics techniques, such as machine learning, deep learning, natural language processing and predictive modelling. As part of data science initiatives, data scientists often must work with large amounts of data to develop and test hypotheses, make inferences and analyze things such as customer and market trends, financial risks, cybersecurity threats, stock trades, equipment maintenance needs and medical conditions.

What is a Research Scientist?

A research scientist is a professional who gathers information by performing both theoretical and experimental research in a specific scientific field. A research scientist can work in many different areas, such as medicine, natural science, computer science, and social science. Research scientists collect data, interpret the results of experiments, and form hypotheses in an attempt to answer questions about the natural world or the man-made world, depending on their field. They can be found working in government agencies, as well as private companies.


Data and research scientists have a wide range of career options available to them, spanning various industries such as technology, engineering, medicine, banking, and business. Typically, data scientists tend to work in the private sector, whether it’s for companies, private research facilities, or on a freelance basis. However, it’s not uncommon for data scientists to work for government agencies as well. Research scientists, on the other hand, can work in both the commercial sector and as government consultants, but a large proportion of them are employed by academic institutions. Research scientists who work in universities and colleges have additional responsibilities compared to their counterparts in industry. They have to balance their research activities with teaching and mentoring students.


To become a Data Scientist, you don’t necessarily need an advanced degree, but most job postings prefer applicants with a master’s degree. On the other hand, Research Scientists typically hold a doctorate in their field of study. Although some may have a master’s degree, most require a doctorate to gain the expertise needed for their work. Research Scientists focus on a specific domain of knowledge, conducting extensive research on complex issues. Many of them also have additional responsibilities as educators in higher education. Pursuing a doctorate in their area of expertise equips them with the knowledge and skills to formulate significant research inquiries and design studies and experiments to address those questions.

These professions also differ in their level of practical application. While data scientists work on projects with immediate applications, research scientists often study theoretical concepts that may not have a direct, practical impact on the real world. For example, an astrophysics research scientist strives to understand how physics relates to the interpretation of astronomical observations. In this process, a data scientist’s role might be to develop a computer program that assists the research scientist in testing models and analyzing data.


Both Data Scientists and Research Scientists share similar responsibilities. They both analyze information to identify trends and develop theories based on their findings. However, their work is focused on different goals. Data Scientists typically work for organizations and use their findings to improve the organization’s bottom line. Research Scientists, on the other hand, work on theories and their research either supports or refutes these theories, which can lead to the development of new theories.

When considering a career in science, it’s important to think about your interests and professional goals. Data and Research Scientists can specialize in different areas, so you should choose a path that aligns with your interests. If you enjoy working with complex computer systems and designing processes that collect, analyze, and model data, then a career as a Data Scientist might be right for you. Alternatively, if you enjoy studying theoretical concepts and aspire to work in higher education, a career as a Research Scientist could be a good fit.

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Data Science + AI and How it May Transform Science Fiction Into Reality

Time travel, black holes, and other dimensions have always fascinated humans, but until recently, they were considered topics for science fiction rather than reality. However, with the advent of data science, machine learning, artificial intelligence, it is seemingly more possible that humans may eventually be able to explore these realms.

Data science involves extracting insights from large and complex data sets using statistical and computational techniques. By applying these methods to information about the universe, we can gain a better understanding of how time, black holes, and other dimensions function. For example, analyzing data from gravitational waves may reveal new information about the behavior of black holes.

Machine learning and artificial intelligence can enhance the power of data science by automating the process of identifying patterns and making predictions. These technologies can analyze vast amounts of data more quickly and accurately than humans can. They can also improve our ability to model complex systems, such as the behavior of particles in a black hole.

Voice interfaces, such as Apple’s Siri, or Ironman’s Jarvis can make it easier to access and analyze large amounts of data. Instead of typing a search query, users can simply ask their device to provide information about a specific topic. This feature could be particularly useful for scientists trying to sift through vast amounts of data to find relevant information about time travel, black holes, or other dimensions. It is even possible to visualize these in VR or AR interfaces.

Modern technological advancements, such as quantum computing, may allow us to simulate and explore different dimensions and timelines (if we haven’t already). Quantum computers are designed to solve complex problems by processing information in multiple states simultaneously, making it possible to explore different scenarios that would be impossible to simulate with classical computers.

In conclusion our advancements in the above areas over the last 2 decades have the potential to allow humans to truly understand what else may be out there and what “out there” really is. While this may still seem like science fiction, it may be possible in the not-too-distant future. By leveraging the power of technology to analyze vast amounts of data and simulate complex systems, we may one day unlock the secrets of the universe.


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8 Pillars Of Data Science: Volume 8 – Deployment

The concept of deployment in data science refers to the application of a model for prediction using new data. Building a model is generally not the end of the project. Even if the purpose of the model is to increase knowledge of the data, the knowledge gained will need to be organized and presented in a way that the customer can use it. Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data science process.

The deployment of a machine learning (ML) models to production starts with actually building the model, which can be done in several ways and with many tools. The approach and tools used at the development stage are very important at ensuring the smooth integration of the basic units that make up the machine learning pipeline. If these are not put into consideration before starting a project, there’s a huge chance of you ending up with an ML system having low efficiency and high latency. For instance, using a function that has been deprecated might still work, but it tends to raise warnings and, as such, increases the response time of the system. The first thing to do in order to ensure this good integration of all system units is to have a system architecture (blueprint) that shows the end-to-end integration of each logical part in the system.
One of the primary options for cloud-based deployment of ML models, along with others such as AWS, Microsoft Azure, Google Cloud Platform (GCP) etc.


AWS – AWS is a cloud computing service that provides on-demand computing resources for storage, networking, Machine learning, etc on a pay-as-you-go pricing model. AWS is a premier cloud computing platform around the globe, and most organization uses AWS for global networking and data storage. The article is for those Machine learning practitioners who know the model building and even they have deployed some projects on other platforms but want to learn how to deploy on major cloud platforms like AWS.

Azure – Azure Machine Learning is an integrated, end-to-end data science and advanced analytics solution. It enables data scientists to prepare data, develop experiments, and deploy models at cloud scale.

The main components of Azure Machine Learning are:

  • Azure Machine Learning Workbench
  • Azure Machine Learning Experimentation Service
  • Azure Machine Learning Model Management Service
  • Microsoft Machine Learning Libraries for Apache Spark (MMLSpark Library)
  • Visual Studio Code Tools for AI

Together, these applications and services help significantly accelerate your data science project development and deployment.

GCP – An AI platform that makes it easy for machine learning developers, data scientists, and data engineers to take their ML projects from ideation to production and deployment, quickly and cost-effectively. From data engineering to “no lock-in” flexibility, Google’s AI Platform has an integrated toolchain that helps in building and running your own machine learning applications. As such, end-to-end ML model development and deployment is possible on the Google’s AI Platform without the need for external tools. The advantage of this is that you don’t need to worry about choosing the best tool to get each job done, and how well each unit integrates with the larger system.

With GCP, depending on how you choose to have your model deployed, there are basically 3 options which are:

  • Google AI Platform
  • Google Cloud Function
  • Google App Engine

Free Courses to upskill yourself:


Deployment is a crucial step in the data science process that involves applying a model for prediction using new data. Smooth integration of the basic units that make up the machine learning pipeline is essential for efficient and low-latency performance of the deployed system. Cloud-based deployment of ML models provides a convenient and cost-effective solution, and major platforms like AWS, Azure, and GCP offer a range of tools and services to accelerate your data science project development and deployment. To upskill yourself, you can take advantage of free courses offered by IBM, Google, AWS, and Microsoft. Keep learning and advancing your skills to stay ahead in this fast-evolving field!

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8 Pillars Of Data Science: Volume 7 – Web Scraping

Data science has become a big part of today’s world. Many big tech companies have data scientists on their teams to help develop their products and services. Data science allows companies to constantly create innovative products that we consumers will spend money on and is worth millions and even billions. Web scraping refers to the extraction of data from a website.

Web scraping refers to the data that is taken from a website. This information is collected and then exported into a format that is more useful for the user. Be it a spreadsheet or an API. Web scraping can be done in many different ways, such as manual data gathering (simple copy/paste), custom scripts, or web scraping tools, such as ParseHub. But in most cases, web scraping is not a simple task. Websites come in many shapes and forms, as a result, web scrapers vary in functionality and features.

Web scraping 

Although it is possible to perform web scraping manually, it’s not always practical. Using machine learning algorithms to automate the web-scraping process is faster and easier and saves time and resources. Data scientists can compile much more information using a web crawler than a manual process. In addition, automated web scraping allows data scientists to seamlessly extract data from websites and upload it into a categorized system for a more organized database.

Web scraping is a required skill for collecting data from online sources—which include a mix of text, images, and links to other pages—in formats that correspond to different programming languages. Web-based data is less structured than numerical data or plain text and requires a method of translating one type of data format into another. Automated web scraping compiles and transposes unstructured HTML into structured rows-and-columns data making it easier for data scientists to understand and analyze the different data types collected.

Many data scientists create web crawlers and scrapers using Python and data science libraries. Among the different types of web crawlers are those programmed to collect data from specific URLs, on a general topic, or to update previously collected web data. Web crawlers can be developed with many programming languages, but Python’s open-source resources and active user community have ensured that there are multiple Python libraries and tools available to do the job. For example, BeautifulSoup is one of many Python data science libraries for HTML and XML data extraction.

BeautifulSoup – BeautifulSoup is used extract information from the HTML and XML files. It provides a parse tree and the functions to navigate, search or modify this parse tree. Beautiful Soup is a Python library used to pull the data out of HTML and XML files for web scraping purposes. It produces a parse tree from page source code that can be utilized to drag data hierarchically and more legibly. It was first presented by Leonard Richardson, who is still donating to this project, and this project is also supported by Tide lift (a paid subscription tool for open-source supervision). Beautiful soup3 was officially released in May 2006, Latest version released by Beautiful Soup is 4.9.2, and it supports Python 3 and Python 2.4 as well.

Scrapy – Scrapy is a free and open-source web crawling framework written in Python. It is a fast, high-level framework used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing. Scrapy uses spiders to define how a site should be scraped for information. It lets us determine how we want a spider to crawl, what information we want to extract, and how we can extract it.

URLLIB – Urllib is a Python module that can be used for opening URLs. It defines functions and classes to help in URL actions. With Python you can also access and retrieve data from the internet like XML, HTML, JSON, etc.

Free Courses to upskill yourself:


Web scraping has become an essential skill for data scientists in today’s world. With the vast amount of information available online, web scraping allows for automated data extraction from websites and the creation of a structured database that is more manageable and easier to analyze. While web scraping can be done manually, using machine learning algorithms and Python libraries such as BeautifulSoup, Scrapy, and urllib, can automate the process and save time and resources. Additionally, data scientists can further their knowledge and skills in web scraping through various free courses available online, such as those available on Coursera.

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8 Pillars Of Data Science: Volume 6 – Integrated Development Environment (IDE)

Integrated Development Environments (IDEs) are coding tools that make writing, debugging, and testing your code easier. It increases developer productivity by combining capabilities such as software editing, building, testing, and packaging in an easy-to-use application. Just as writers use text editors and accountants use spreadsheets, software developers use IDEs to make their job easier. Many provide helpful features like code completion, syntax highlighting, debugging tools, variable explorers, visualization tools, and many other features.  

You can use any text editor to write code. However, most integrated development environments (IDEs) include functionality that goes beyond text editing. They provide a central interface for common developer tools, making the software development process much more efficient. Developers can start programming new applications quickly instead of manually integrating and configuring different software. They also don’t have to learn about all the tools and can instead focus on just one application. 

Types of IDEs: 

Local IDEs: Developers install and run local IDEs directly on their local machines. They also must download and install various additional libraries depending on their coding preferences, project requirements, and development language. While local IDEs are customizable and do not require an internet connection once installed, they present several challenges: 

  • They can be time consuming and difficult to set up. 
  • They consume local machine resources and can slow down machine performance significantly. 
  • Configuration differences between the local machine and the production environment can give rise to software errors. 

Cloud IDEs: Developers use cloud IDEs to write, edit, and compile code directly in the browser so that they don’t need to download software on their local machines. Cloud-based IDEs have several advantages over traditional IDEs. The following are some of these advantages: 

Standardized development environment: Software development teams can centrally configure a cloud-based IDE to create a standard development environment. This method helps them avoid errors that might occur due to local machine configuration differences. 

Platform independence: Cloud IDEs work on the browser and are independent of local development environments. This means they connect directly to the cloud vendor’s platform, and developers can use them from any machine. 

Better performance: Building and compiling functions in an IDE requires a lot of memory and can slow down the developer’s computer. The cloud IDE uses compute resources from the cloud and frees up the local machine’s resources. 

Courses to upskill yourself on IDEs:


In summary, Integrated Development Environments (IDEs) are essential tools for software developers that provide a centralized interface for common developer tools. IDEs increase developer productivity by combining software editing, building, testing, and packaging in an easy-to-use application. IDEs offer several helpful features like code completion, syntax highlighting, debugging tools, variable explorers, visualization tools, and many others.

There are two types of IDEs: local IDEs and cloud IDEs. Local IDEs are installed directly on a developer’s machine, while cloud IDEs allow developers to write, edit, and compile code directly in the browser without downloading software on their local machines. Cloud IDEs provide several advantages such as a standardized development environment, platform independence, and better performance.

The courses linked in this article are free and have been selected by us to help software developers improve their IDE skills.

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8 Pillars Of Data Science: Volume 5 – Machine Learning

Machine Learning is the core subarea of artificial intelligence. It makes computers get into a self-learning mode without complicated programming. When ingesting new data, these computers learn, grow, change, and develop by themselves. The concept of machine learning has been around for a while now. However, the ability to automatically and quickly apply mathematical calculations to big data is now gaining a bit of momentum. Machine learning has been used in several places like the self-driving cars, the online recommendation engines – friend recommendations on Facebook, offer suggestions from Amazon, and in cyber fraud detection.

Data analysis has traditionally been characterized by the trial-and-error approach – one that becomes impossible to use when there are significant and diverse data sets in question. The availability of more data is directly proportional to the difficulty of bringing in new predictive models that work accurately. Traditional statistical solutions are more focused on static analysis that is limited to the analysis of samples that are frozen in time. This could result in unreliable and inaccurate conclusions.

Coming as a solution to all this chaos is Machine Learning proposing smart alternatives to analyzing vast volumes of data. It is a leap forward from computer science, statistics, and other emerging applications in the industry. Machine learning can produce accurate results and analysis by developing efficient and fast algorithms and data-driven models for real-time processing of this data.

Common Machine Learning models  

Binary Classification: In machine learning, binary classification is a supervised learning algorithm that categorizes new observations into one of two classes.  

Examples of Binary Classification Problems 

  • “Is this email spam or not spam?” 
  • “Will you recommend this to a friend?” 
  • “Is this review written by a customer or a robot? 

Regression: A technique for investigating the relationship between independent variables or features and a dependent variable or outcome. It’s used as a method for predictive modelling in machine learning, in which an algorithm is used to predict continuous outcomes.

Examples of this are:  

  • Forecasting continuous outcomes like house prices, stock prices, or sales.  
  • Predicting the success of future retail sales or marketing campaigns to ensure resources are used effectively.  
  • Predicting customer or user trends, such as on streaming services or e-commerce websites. 

Multiclass Classification: Multiclass classification is a machine learning classification task that consists of more than two classes, or outputs. For example, using a model to identify animal types in images from an encyclopedia is a multiclass classification example because there are many different animal classifications that each image can be classified as. Multiclass classification also requires that a sample only have one class (ie. a dolphin is only a dolphin; it is not also a gator). 

Common Machine Learning Algorithms

Reinforcement Learning: Reinforcement learning is a machine learning training method based on rewarding desired behaviors and/or punishing undesired ones. In general, a reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error. In reinforcement learning, developers devise a method of rewarding desired behaviors and punishing negative behaviors. This method assigns positive values to the desired actions to encourage the agent and negative values to undesired behaviors. This programs the agent to seek long-term and maximum overall reward to achieve an optimal solution. 

These long-term goals help prevent the agent from stalling on lesser goals. With time, the agent learns to avoid the negative and seek the positive. This learning method has been adopted in artificial intelligence (AI) as a way of directing unsupervised machine learning through rewards and penalties. 

Deep Learning: Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy. 

“Deep learning drives many artificial intelligence (AI) applications and services that improve automation, performing analytical and physical tasks without human intervention. Deep learning technology lies behind everyday products and services (such as digital assistants, voice-enabled TV remotes, and credit card fraud detection) as well as emerging technologies (such as self-driving cars).”

Clustering: Clustering or cluster analysis is a machine learning technique, which groups the unlabelled dataset. It can be defined as “A way of grouping the data points into different clusters, consisting of similar data points. The objects with the possible similarities remain in a group that has less or no similarities with another group.” t does it by finding some similar patterns in the unlabelled dataset such as shape, size, color, behavior, etc., and divides them as per the presence and absence of those similar patterns. It is an unsupervised learning method, hence no supervision is provided to the algorithm, and it deals with the unlabeled dataset. After applying this clustering technique, each cluster or group is provided with a cluster-ID. ML system can use this id to simplify the processing of large and complex datasets. 

Free Courses to upskill your knowledge in Machine Learning:


Machine Learning is a powerful tool that is transforming the way we analyze and process data. Its ability to learn, adapt and develop by itself makes it an essential component of artificial intelligence. Traditional statistical methods are limited to static analysis and small data sets, while Machine Learning can process vast amounts of data in real-time, producing accurate results and analysis.

In this article, we discussed some of the common Machine Learning models and algorithms like Binary Classification, Regression, Multiclass Classification, Reinforcement Learning, Deep Learning, and Clustering. Each model has its specific uses, and choosing the right one depends on the task at hand. Machine Learning is a rapidly growing field with many exciting opportunities Upskilling yourself in this area is definitely worth it.  There are several free online courses available that can help you get started on your journey to mastering this field.

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8 Pillars Of Data Science: Volume 4 – Mathematics

Mathematics is very important in the field of data science as concepts within mathematics aid in identifying patterns and assist in creating algorithms. The understanding of various notions of Statistics and Probability Theory are key for the implementation of such algorithms in data science. Notions include: Regression, Maximum Likelihood Estimation, the understanding of distributions (Binomial, Bernoulli, Gaussian (Normal)) and Bayes’ Theorem. “Mathematics in data science and machine learning is not about crunching numbers, but about what is happening, why it’s happening, and how we can play around with different things to obtain the results we want.” Some common areas of mathematics that aid in data science work are (but not limited to) statistics, linear algebra and Differential Calculus.

Now let’s take a deeper dive into these areas of mathematics and some courses to help you upskill yourself:


In data science, statistics is at the core of sophisticated machine learning algorithms, capturing and translating data patterns into actionable evidence. Data scientists use statistics to gather, review, analyze, and draw conclusions from data, as well as apply quantified mathematical models to appropriate variables. Data scientists work as programmers, researchers, business executives, and more. However, what all these areas have in common is a basis of statistics. Thus, statistics in data science is as necessary as understanding programming languages.

Statistical functions are used in data science to analyze raw data, build data models, and infer results. Below is a list of the key statistical terms: Population: the source of data to be collected; Sample: a portion of the population; Variable: any data item that can be measured or counted; Quantitative analysis (statistical): collecting and interpreting data with patterns and data visualization; Qualitative analysis (non-statistical): producing generic information from other non-data forms of media.

Here are some courses to upskill your Statistics skills:

Linear Algebra:

Linear Algebra is a branch of mathematics that is extremely useful in data science and machine learning. Linear algebra is the most important math skill in machine learning. Most machine learning models can be expressed in matrix form. A dataset itself is often represented as a matrix. “With an understanding of Linear Algebra, you will be able to develop a better intuition for machine learning and deep learning algorithms and not treat them as black boxes. This would allow you to choose proper hyperparameters and develop a better model. You would also be able to code algorithms from scratch and make your own variations to them as well.” Here are a few topics you need to be familiar with (not limited to): Vectors, Matrices, Transpose of a matrix, Inverse of a matrix, Determinant of a matrix and Trace of a matrix.

Here are some courses to upskill your Linear Algebra skills:


Calculus is an intrinsic field of mathematics and especially in many machine learning algorithms that you cannot skip over if you want to learn to learn the essence of Data Science. A machine learning algorithm (such as classification, clustering or regression) uses a training dataset to determine weight factors that can be applied to unseen data for predictive purposes. Behind every machine learning model is an optimization algorithm that relies heavily on calculus. Therefore it is important to have fundamental knowledge in calculus as this would enable a data scientist to have some understanding of the optimization algorithms used in data science and machine learning. There are two types of calculus that are worth highlights: Differential Calculus and Integral Calculus. Differential Calculus cuts something into small pieces to find how it changes. Integral Calculus joins (integrates) the small pieces together to find how much there is.

Here are some courses to upskill your Calculus skills: 


In the field of data science, mathematics plays a crucial role in identifying patterns, creating algorithms, and developing insights. Statistics, linear algebra, and calculus are some of the key areas of mathematics that aid data science work. Upgrading skills in these areas is essential for aspiring and current data scientists. Several courses, such as those offered by Coursera can help individuals upskill themselves.

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